Publications

Assessment of differentially private fine-tuning of large language models for synthetic clinical note generation
Atiquer Rahman Sarkar
Fatima Jahan Sarmin
Djedjiga Mouheb
Benjamin C. M. Fung
Noman Mohammed
2026-03-31
Intelligent Systems with Applications (publié)
doi.org
CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
Tara Saba
Anne Ouyang
Xujie Si
Fan Long
High-performance GPU kernels are critical to modern machine learning systems, yet developing efficient implementations remains a challenging… (voir plus), expert-driven process due to the tight coupling between algorithmic structure, memory hierarchy usage, and hardware-specific optimizations. Recent work has explored using large language models (LLMs) to generate GPU kernels automatically, but generated implementations often struggle to maintain correctness and achieve competitive performance across iterative refinements. We present CuTeGen, an agentic framework for automated generation and optimization of GPU kernels that treats kernel development as a structured generate--test--refine workflow. Unlike approaches that rely on one-shot generation or large-scale search over candidate implementations, CuTeGen focuses on progressive refinement of a single evolving kernel through execution-based validation, structured debugging, and staged optimization. A key design choice is to generate kernels using the CuTe abstraction layer, which exposes performance-critical structures such as tiling and data movement while providing a more stable representation for iterative modification. To guide performance improvement, CuTeGen incorporates workload-aware optimization prompts and delayed integration of profiling feedback. Experimental results on matrix multiplication and activation workloads demonstrate that the framework produces functionally correct kernels and achieves competitive performance relative to optimized library implementations.
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
Model Merging via Data-Free Covariance Estimation
Marawan Gamal Abdel Hameed
Derek Tam
Pascal Jr Tikeng Notsawo
Colin Raffel
Guillaume Rabusseau
Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While s… (voir plus)ome merging methods can approach the performance of multitask training, they are often heuristically motivated and lack theoretical justification. A principled alternative is to pose model merging as a layer-wise optimization problem that directly minimizes interference between tasks. However, this formulation requires estimating per-layer covariance matrices from data, which may not be available when performing merging. In contrast, many of the heuristically-motivated methods do not require auxiliary data, making them practically advantageous. In this work, we revisit the interference minimization framework and show that, under certain conditions, covariance matrices can be estimated directly from difference matrices, eliminating the need for data while also reducing computational costs. We validate our approach across vision and language benchmarks on models ranging from 86M parameters to 7B parameters, outperforming previous data-free state-of-the-art merging methods
2026-03-31
arXiv (prépublication)
doi.org
arxiv.org
Primary large-cell neuroendocrine carcinoma of the prostate and its nursing care: A systematic review
Mingli Wang
Xuemei Zhang
Lifang Pan
Jun Ding
2026-03-31
Asian Journal of Surgery (publié)
doi.org
CLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition
Muhammad Zeeshan
Masoumeh Sharafi
Benoit Savary
Alessandro L. Koerich
Marco Pedersoli
Eric Granger
Personalization in emotion recognition (ER) is essential for an accurate interpretation of subtle and subject-specific expressive patterns. … (voir plus)Recent advances in vision-language models (VLMs) such as CLIP demonstrate strong potential for leveraging joint image-text representations in ER. However, CLIP-based methods either depend on CLIP's contrastive pretraining or on LLMs to generate descriptive text prompts, which are noisy, computationally expensive, and fail to capture fine-grained expressions, leading to degraded performance. In this work, we leverage Action Units (AUs) as structured textual prompts within CLIP to model fine-grained facial expressions. AUs encode the subtle muscle activations underlying expressions, providing localized and interpretable semantic cues for more robust ER. We introduce CLIP-AU, a lightweight AU-guided temporal learning method that integrates interpretable AU semantics into CLIP. It learns generic, subject-agnostic representations by aligning AU prompts with facial dynamics, enabling fine-grained ER without CLIP fine-tuning or LLM-generated text supervision. Although CLIP-AU models fine-grained AU semantics, it does not adapt to subject-specific variability in subtle expressions. To address this limitation, we propose CLIP-AUTT, a video-based test-time personalization method that dynamically adapts AU prompts to videos from unseen subjects. By combining entropy-guided temporal window selection with prompt tuning, CLIP-AUTT enables subject-specific adaptation while preserving temporal consistency. Our extensive experiments on three challenging video-based subtle ER datasets, BioVid, StressID, and BAH, indicate that CLIP-AU and CLIP-AUTT outperform state-of-the-art CLIP-based FER and TTA methods, achieving robust and personalized subtle ER. Our code is publicly available at: https://github.com/osamazeeshan/CLIP-AUTT.
2026-03-29
arXiv (prépublication)
doi.org
arxiv.org
Stop Probing, Start Coding: Why Linear Probes and Sparse Autoencoders Fail at Compositional Generalisation
Vitoria Barin Pacela
Shruti Joshi
Isabela Camacho
Simon Lacoste-Julien
David Klindt
The linear representation hypothesis states that neural network activations encode high-level concepts as linear mixtures. However, under su… (voir plus)perposition, this encoding is a projection from a higher-dimensional concept space into a lower-dimensional activation space, and a linear decision boundary in the concept space need not remain linear after projection. In this setting, classical sparse coding methods with per-sample iterative inference leverage compressed sensing guarantees to recover latent factors. Sparse autoencoders (SAEs), on the other hand, amortise sparse inference into a fixed encoder, introducing a systematic gap. We show this amortisation gap persists across training set sizes, latent dimensions, and sparsity levels, causing SAEs to fail under out-of-distribution (OOD) compositional shifts. Through controlled experiments that decompose the failure, we identify dictionary learning -- not the inference procedure -- as the binding constraint: SAE-learned dictionaries point in substantially wrong directions, and replacing the encoder with per-sample FISTA on the same dictionary does not close the gap. An oracle baseline proves the problem is solvable with a good dictionary at all scales tested. Our results reframe the SAE failure as a dictionary learning challenge, not an amortisation problem, and point to scalable dictionary learning as the key open problem for sparse inference under superposition.
2026-03-29
arXiv (prépublication)
doi.org
arxiv.org
Communicating about Space: Language-Mediated Spatial Integration Across Partial Views
Ankur Sikarwar
Debangan Mishra
Sudarshan Nikhil
Ponnurangam Kumaraguru
Aishwarya Agrawal
Humans build shared spatial understanding by communicating partial, viewpoint-dependent observations. We ask whether Multimodal Large Langua… (voir plus)ge Models (MLLMs) can do the same, aligning distinct egocentric views through dialogue to form a coherent, allocentric mental model of a shared environment. To study this systematically, we introduce COSMIC, a benchmark for Collaborative Spatial Communication. In this setting, two static MLLM agents observe a 3D indoor environment from different viewpoints and exchange natural-language messages to solve spatial queries. COSMIC contains 899 diverse scenes and 1250 question-answer pairs spanning five tasks. We find a capability hierarchy, MLLMs are most reliable at identifying shared anchor objects across views, perform worse on relational reasoning, and largely fail at building globally consistent maps, performing near chance, even for frontier models. Moreover, we find thinking capability yields gains in anchor grounding, but is insufficient for higher-level spatial communication. To contextualize model behavior, we collect 250 human-human dialogues. Humans achieve 95% aggregate accuracy, while the best model, Gemini-3-Pro-Thinking, reaches 72%, leaving substantial room for improvement. Moreover, human conversations grow more precise as partners align on a shared spatial understanding, whereas MLLMs keep exploring without converging, suggesting limited capacity to form and sustain a robust shared mental model throughout the dialogue. Our code and data is available at https://github.com/ankursikarwar/Cosmic.
2026-03-27
arXiv (prépublication)
doi.org
arxiv.org
Additional file 1 of Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Supplementary Material 1
2026-03-26
Figshare (inconnu)
doi.org
Additional file 2 of Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Supplementary Material 2
2026-03-26
Figshare (inconnu)
doi.org
Efficient CMOS Invertible Logic Using Stochastic Computing
Sean C. Smithson
Naoya Onizawa
Brett H. Meyer
Warren J. Gross
Takahiro Hanyu
Invertible logic can operate in one of two modes: 1) a forward mode, in which inputs are presented and a single, correct output is produced,… (voir plus) and 2) a reverse mode, in which the output is fixed and the inputs take on values consistent with the output. It is possible to create invertible logic using various Boltzmann machine configurations. Such systems have been shown to solve certain challenging problems quickly, such as factorization and combinatorial optimization. In this paper, we show that invertible logic can be implemented using simple spiking neural networks based on stochastic computing. We present a design methodology for invertible stochastic gates, which can be implemented using a small amount of CMOS hardware. We demonstrate that our design can not only correctly implement basic gates with invertible capability, but can also be extended to construct invertible stochastic adder and multiplier circuits. Experimental results are presented which demonstrate correct operation of synthesizable invertible circuitry performing both multiplication and factorization, along with fabricated ASIC measurement results for an invertible multiplier circuit.
2026-03-26
arXiv (prépublication)
doi.org
arxiv.org
Impact of WHO AWaRe Antibiotic Handbook training on antibiotics prescribing knowledge among private primary care providers: a vignette-based, prep–post pilot study in Patna, India
Poshan Thapa
Prachi Shukla
Chandrashekhar Joshi
Sena Sayood
P. Sinha
Diwash Timilsina
Mili Dutta
Madhukar Pai
Samira Abbasgholizadeh Rahimi
Sumanth Gandra
Abstract Introduction Inappropriate antibiotic prescribing is a major concern in low- and middle-income countries (LMICs), particularly at t… (voir plus)he primary care level. The WHO AWaRe Antibiotic Handbook was introduced to promote rational antibiotic use, yet its real-world feasibility and potential impact remain underexplored. Our study evaluated the impact and usefulness of the WHO AWaRe Handbook training among private primary care providers (PCPs) in Patna, India. Methods We conducted a pre–post pilot study among 145 private PCPs (40 formal PCPs (FPs) and 105 informal PCPs (IPs) in Patna, India. Participants received training from an infectious disease physician on the WHO AWaRe Antibiotic Handbook. Antibiotic prescribing knowledge was assessed before and after the intervention using clinical vignettes for four conditions: acute diarrhea, urinary tract infection (UTI), cellulitis, and community-acquired pneumonia (CAP). An endline survey evaluated the perceived usefulness of the intervention. Changes in prescribing knowledge was analyzed using McNemar’s test for paired data. Results The intervention significantly reduced overall antibiotic prescribing knowledge for acute diarrhea ( p = 0.0003) and UTI ( p = 0.0113), with greater reductions among IPs. No significant changes were observed for cellulitis ( p = 0.3692) or CAP ( p = 0.7150). Watch-category antibiotic prescribing significantly decreased for acute diarrhea ( p < 0.0001), with no significant changes for other conditions. IPs showed greater improvements overall compared to FPs. The majority of PCPs (75%; n = 107) rated the training as moderately or very useful. Conclusion Training private PCPs using the WHO AWaRe Handbook improved antibiotic prescribing knowledge for some common conditions, particularly among IPs. Future research should combine training with strategies that address broader contextual barriers, alongside tailored reinforcement interventions, and longer-term follow-up.
2026-03-26
Antimicrobial Resistance & Infection Control (publié)
doi.org
Neighbor-Aware Localized Concept Erasure in Text-to-Image Diffusion Models
Zhuan Shi
Alireza Dehghanpour Farashah
Rik de Vries
Golnoosh Farnadi
Concept erasure in text-to-image diffusion models seeks to remove undesired concepts while preserving overall generative capability. Localiz… (voir plus)ed erasure methods aim to restrict edits to the spatial region occupied by the target concept. However, we observe that suppressing a concept can unintentionally weaken semantically related neighbor concepts, reducing fidelity in fine-grained domains. We propose Neighbor-Aware Localized Concept Erasure (NLCE), a training-free framework designed to better preserve neighboring concepts while removing target concepts. It operates in three stages: (1) a spectrally-weighted embedding modulation that attenuates target concept directions while stabilizing neighbor concept representations, (2) an attention-guided spatial gate that identifies regions exhibiting residual concept activation, and (3) a spatially-gated hard erasure that eliminates remaining traces only where necessary. This neighbor-aware pipeline enables localized concept removal while maintaining the surrounding concept neighborhood structure. Experiments on fine-grained datasets (Oxford Flowers, Stanford Dogs) show that our method effectively removes target concepts while better preserving closely related categories. Additional results on celebrity identity, explicit content and artistic style demonstrate robustness and generalization to broader erasure scenarios.
2026-03-26
arXiv (prépublication)
doi.org
arxiv.org